Integrating Machine Learning Techniques to Adapt Protocols for QoS-enabled Distributed Real-time and Embedded Publish/Subscribe Middleware

摘要

Quality-of-service (QoS)-enabled publish/subscribe (pub/sub) middleware provides the infra-structure needed to disseminate data predictably, reliably, and scalably in distributed real-time and embedded (DRE) systems. Maintaining QoS properties as the operating environ¬ment fluctuates is challenging, however, since the chosen mechanism (e.g., transport protocol or caching algorithm for data persistence) may no longer provide the needed QoS. Moreover, some adaptation approaches are tailored for particular types of operating environments, such as environments whose configuration properties (e.g., number of data receivers or data sending rate) are known prior to runtime versus unknown until runtime. For DRE pub/sub systems operating in dynamic environments, adjustments to mechanisms must be timely, accurate for known environments, and resilient to environments unknown until runtime. Several adaptation approaches, such as policy-based [1] and reinforcement learning [2] have been developed to ensure end-to-end quality-of-service (QoS) for enterprise distributed systems in dynamic operating environments. Not all approaches are applicable for DRE pub/sub systems, however, due to their stringent accuracy, timeliness, and development complexity requirements. Supervised machine learning techniques, such as artificial neural networks (ANNs) [3] and support vector machines (SVMs) [4], are promising approaches to address the accuracy, time complexity, and development complexity concerns of adaptive enterprise DRE systems. This article describes the results of research that (1) empirically evaluates supervised machine learning techniques used to adapt the transport protocols of QoS-enabled pub/sub middleware autonomically in a dynamic environment and (2) integrates multiple techniques to increase accuracy for environments known a priori and not known until runtime. Our results show that both ANNs and SVMs provide constant time complexity, low latency, and reduced de-velopment complexity. ANNs are generally more accurate in providing adaptation guidance for environments whose properties are known prior to runtime and provide sub-?sec response times, whereas SVMs provide higher accuracy with ?sec latencies for environments whose properties are not known until runtime. Both approaches can be leveraged together with QoS-enabled pub/sub middleware to address the timeliness, accuracy, and development com-plexity needs of enterprise DRE systems executing in dynamic environments.